Class swarmauri_standard.inner_products.WeightedL2InnerProduct.WeightedL2InnerProduct

swarmauri_standard.inner_products.WeightedL2InnerProduct.WeightedL2InnerProduct

WeightedL2InnerProduct(weight_function, **kwargs)

Bases: InnerProductBase

Weighted L2 inner product for real/complex functions.

This class implements a weighted L2 inner product, which defines an inner product with position-dependent weights for weighted L2 spaces. The weight function must be strictly positive.

Attributes

type : Literal["WeightedL2InnerProduct"] The type identifier for this inner product implementation resource : str The resource type identifier, defaulting to INNER_PRODUCT weight_function : Callable[[Any], Union[float, np.ndarray]] A function that returns a positive weight at each position

Initialize the WeightedL2InnerProduct with a weight function.

Parameters

weight_function : Callable[[Any], Union[float, np.ndarray]] A function that returns a positive weight at each position **kwargs : Dict[str, Any] Additional keyword arguments

Raises

ValueError If the weight function is not provided

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def __init__(
    self,
    weight_function: Callable[[Any], Union[float, np.ndarray]],
    **kwargs: Dict[str, Any],
):
    """
    Initialize the WeightedL2InnerProduct with a weight function.

    Parameters
    ----------
    weight_function : Callable[[Any], Union[float, np.ndarray]]
        A function that returns a positive weight at each position
    **kwargs : Dict[str, Any]
        Additional keyword arguments

    Raises
    ------
    ValueError
        If the weight function is not provided
    """
    if weight_function is None:
        logger.error("Weight function must be provided")
        raise ValueError("Weight function must be provided")

    kwargs["weight_function"] = weight_function
    super().__init__(**kwargs)

    logger.info("WeightedL2InnerProduct initialized with custom weight function")

type class-attribute instance-attribute

type = 'WeightedL2InnerProduct'

weight_function instance-attribute

weight_function

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = INNER_PRODUCT.value

version class-attribute instance-attribute

version = '0.1.0'

compute

compute(a, b)

Compute the weighted L2 inner product between two objects.

For functions, the inner product is computed as: = ∫ a(x) * conj(b(x)) * w(x) dx

For vectors/matrices, the inner product is: = sum(a * conj(b) * w)

Parameters

a : Union[Vector, Matrix, Callable] The first object for inner product calculation b : Union[Vector, Matrix, Callable] The second object for inner product calculation

Returns

complex The inner product value

Raises

TypeError If the input types are not supported ValueError If the dimensions of inputs don't match

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def compute(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> complex:
    """
    Compute the weighted L2 inner product between two objects.

    For functions, the inner product is computed as:
    <a, b> = ∫ a(x) * conj(b(x)) * w(x) dx

    For vectors/matrices, the inner product is:
    <a, b> = sum(a * conj(b) * w)

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The first object for inner product calculation
    b : Union[Vector, Matrix, Callable]
        The second object for inner product calculation

    Returns
    -------
    complex
        The inner product value

    Raises
    ------
    TypeError
        If the input types are not supported
    ValueError
        If the dimensions of inputs don't match
    """
    logger.debug(
        f"Computing weighted L2 inner product between {type(a)} and {type(b)}"
    )

    # Handle callable functions (requires numerical integration)
    if callable(a) and callable(b):
        # This is a simplified implementation
        # In a real-world scenario, you would use numerical integration
        # over the domain with appropriate quadrature points

        # For demonstration, we'll use a simple grid
        # In practice, replace with proper numerical integration
        try:
            # Example: integrate over [0,1] with 1000 points
            # This is a simplification; actual implementation would depend on the domain
            x = np.linspace(0, 1, 1000)
            weights = self.weight_function(x)

            # Check positivity of weights
            if np.any(weights <= 0):
                logger.error("Weight function returned non-positive values")
                raise ValueError("Weight function must be strictly positive")

            # Compute function values
            a_values = np.array([a(xi) for xi in x])
            b_values = np.array([b(xi) for xi in x])

            # Compute inner product with trapezoidal rule
            dx = x[1] - x[0]
            result = np.sum(a_values * np.conjugate(b_values) * weights) * dx
            return complex(result)
        except Exception as e:
            logger.error(
                f"Error computing inner product for callable functions: {str(e)}"
            )
            raise

    # Handle numpy arrays or vectors
    elif isinstance(a, (np.ndarray, list)) and isinstance(b, (np.ndarray, list)):
        a_array = np.array(a)
        b_array = np.array(b)

        if a_array.shape != b_array.shape:
            logger.error(f"Dimension mismatch: {a_array.shape} vs {b_array.shape}")
            raise ValueError(
                f"Dimensions must match: {a_array.shape} vs {b_array.shape}"
            )

        # Get weights for each position
        # For simplicity, we assume the arrays represent points where we evaluate the weight
        # In practice, this might need to be adapted based on what the arrays represent
        weights = self.weight_function(
            np.arange(len(a_array)) if a_array.ndim == 1 else None
        )

        # Ensure weights have the right shape
        if isinstance(weights, Number):
            weights_array = np.full_like(a_array, weights, dtype=float)
        else:
            weights_array = np.array(weights)
            if weights_array.shape != a_array.shape:
                logger.error(
                    f"Weight shape {weights_array.shape} doesn't match input shape {a_array.shape}"
                )
                raise ValueError(
                    f"Weight shape must match input shape: {weights_array.shape} vs {a_array.shape}"
                )

        # Check positivity of weights
        if np.any(weights_array <= 0):
            logger.error("Weight function returned non-positive values")
            raise ValueError("Weight function must be strictly positive")

        # Compute the weighted inner product
        result = np.sum(a_array * np.conjugate(b_array) * weights_array)
        return complex(result)

    else:
        logger.error(f"Unsupported types: {type(a)} and {type(b)}")
        raise TypeError(
            f"Unsupported types for WeightedL2InnerProduct: {type(a)} and {type(b)}"
        )

check_conjugate_symmetry

check_conjugate_symmetry(a, b)

Check if the weighted L2 inner product satisfies the conjugate symmetry property: = * (complex conjugate).

Parameters

a : Union[Vector, Matrix, Callable] The first object b : Union[Vector, Matrix, Callable] The second object

Returns

bool True if conjugate symmetry holds, False otherwise

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def check_conjugate_symmetry(
    self, a: Union[Vector, Matrix, Callable], b: Union[Vector, Matrix, Callable]
) -> bool:
    """
    Check if the weighted L2 inner product satisfies the conjugate symmetry property:
    <a, b> = <b, a>* (complex conjugate).

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The first object
    b : Union[Vector, Matrix, Callable]
        The second object

    Returns
    -------
    bool
        True if conjugate symmetry holds, False otherwise
    """
    logger.debug(f"Checking conjugate symmetry for {type(a)} and {type(b)}")

    try:
        # Compute <a, b>
        inner_ab = self.compute(a, b)

        # Compute <b, a> and take complex conjugate
        inner_ba_conj = np.conjugate(self.compute(b, a))

        # Check if they are approximately equal
        is_symmetric = np.isclose(inner_ab, inner_ba_conj)

        if not is_symmetric:
            logger.warning(
                f"Conjugate symmetry check failed: <a,b>={inner_ab}, <b,a>*={inner_ba_conj}"
            )

        return is_symmetric
    except Exception as e:
        logger.error(f"Error checking conjugate symmetry: {str(e)}")
        return False

check_linearity_first_argument

check_linearity_first_argument(a1, a2, b, alpha, beta)

Check if the weighted L2 inner product satisfies linearity in the first argument: = alpha + beta.

Parameters

a1 : Union[Vector, Matrix, Callable] First component of the first argument a2 : Union[Vector, Matrix, Callable] Second component of the first argument b : Union[Vector, Matrix, Callable] The second object alpha : float Scalar multiplier for a1 beta : float Scalar multiplier for a2

Returns

bool True if linearity in the first argument holds, False otherwise

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def check_linearity_first_argument(
    self,
    a1: Union[Vector, Matrix, Callable],
    a2: Union[Vector, Matrix, Callable],
    b: Union[Vector, Matrix, Callable],
    alpha: float,
    beta: float,
) -> bool:
    """
    Check if the weighted L2 inner product satisfies linearity in the first argument:
    <alpha*a1 + beta*a2, b> = alpha*<a1, b> + beta*<a2, b>.

    Parameters
    ----------
    a1 : Union[Vector, Matrix, Callable]
        First component of the first argument
    a2 : Union[Vector, Matrix, Callable]
        Second component of the first argument
    b : Union[Vector, Matrix, Callable]
        The second object
    alpha : float
        Scalar multiplier for a1
    beta : float
        Scalar multiplier for a2

    Returns
    -------
    bool
        True if linearity in the first argument holds, False otherwise
    """
    logger.debug(
        f"Checking linearity in first argument with alpha={alpha}, beta={beta}"
    )

    try:
        # For callable functions
        if callable(a1) and callable(a2) and callable(b):
            # Create a linear combination function
            def linear_combo(x):
                return alpha * a1(x) + beta * a2(x)

            # Compute <alpha*a1 + beta*a2, b>
            left_side = self.compute(linear_combo, b)

            # Compute alpha*<a1, b> + beta*<a2, b>
            right_side = alpha * self.compute(a1, b) + beta * self.compute(a2, b)

        # For arrays
        elif (
            isinstance(a1, (np.ndarray, list))
            and isinstance(a2, (np.ndarray, list))
            and isinstance(b, (np.ndarray, list))
        ):
            a1_array = np.array(a1)
            a2_array = np.array(a2)

            # Create linear combination
            linear_combo = alpha * a1_array + beta * a2_array

            # Compute <alpha*a1 + beta*a2, b>
            left_side = self.compute(linear_combo, b)

            # Compute alpha*<a1, b> + beta*<a2, b>
            right_side = alpha * self.compute(a1, b) + beta * self.compute(a2, b)

        else:
            logger.error(
                f"Unsupported types: {type(a1)}, {type(a2)}, and {type(b)}"
            )
            return False

        # Check if they are approximately equal
        is_linear = np.isclose(left_side, right_side)

        if not is_linear:
            logger.warning(
                f"Linearity check failed: <alpha*a1+beta*a2,b>={left_side}, "
                f"alpha*<a1,b>+beta*<a2,b>={right_side}"
            )

        return is_linear
    except Exception as e:
        logger.error(f"Error checking linearity: {str(e)}")
        return False

check_positivity

check_positivity(a)

Check if the weighted L2 inner product satisfies the positivity property: >= 0 and = 0 iff a = 0.

Parameters

a : Union[Vector, Matrix, Callable] The object to check positivity for

Returns

bool True if positivity holds, False otherwise

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def check_positivity(self, a: Union[Vector, Matrix, Callable]) -> bool:
    """
    Check if the weighted L2 inner product satisfies the positivity property:
    <a, a> >= 0 and <a, a> = 0 iff a = 0.

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The object to check positivity for

    Returns
    -------
    bool
        True if positivity holds, False otherwise
    """
    logger.debug(f"Checking positivity for {type(a)}")

    try:
        # Compute <a, a>
        inner_product = self.compute(a, a)

        # Check if it's real (should be for any inner product)
        if not np.isclose(inner_product.imag, 0):
            logger.warning(
                f"Inner product <a,a> has non-zero imaginary part: {inner_product.imag}"
            )
            return False

        # Check if it's non-negative
        is_positive = inner_product.real >= 0

        # For arrays, also check if <a,a> = 0 implies a = 0
        if isinstance(a, (np.ndarray, list)) and np.isclose(inner_product.real, 0):
            a_array = np.array(a)
            is_positive = is_positive and np.allclose(a_array, 0)

        if not is_positive:
            logger.warning(f"Positivity check failed: <a,a>={inner_product.real}")

        return is_positive
    except Exception as e:
        logger.error(f"Error checking positivity: {str(e)}")
        return False

norm

norm(a)

Compute the norm induced by the weighted L2 inner product.

Parameters

a : Union[Vector, Matrix, Callable] The object to compute the norm for

Returns

float The norm value

Raises

ValueError If the inner product is negative (which shouldn't happen for a valid inner product)

Source code in swarmauri_standard/inner_products/WeightedL2InnerProduct.py

def norm(self, a: Union[Vector, Matrix, Callable]) -> float:
    """
    Compute the norm induced by the weighted L2 inner product.

    Parameters
    ----------
    a : Union[Vector, Matrix, Callable]
        The object to compute the norm for

    Returns
    -------
    float
        The norm value

    Raises
    ------
    ValueError
        If the inner product is negative (which shouldn't happen for a valid inner product)
    """
    logger.debug(f"Computing norm for {type(a)}")

    try:
        inner_product = self.compute(a, a)

        # The inner product <a,a> should be real and non-negative
        if inner_product.imag != 0 or inner_product.real < 0:
            logger.error(f"Invalid inner product value: {inner_product}")
            raise ValueError(
                f"Inner product <a,a> must be real and non-negative, got {inner_product}"
            )

        return np.sqrt(inner_product.real)
    except Exception as e:
        logger.error(f"Error computing norm: {str(e)}")
        raise

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger